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Survival prediction and treatment selection in lung cancer care are characterised by high levels of uncertainty. Bayesian Networks (BNs), which naturally reason with uncertain domain knowledge, can be applied to aid lung cancer experts by providing personalised survival estimates and treatment selection recommendations. Based on the English Lung Cancer Database (LUCADA), we evaluate the feasibility of BNs for these two tasks, while comparing the performances of various causal discovery approaches to uncover the most feasible network structure from expert knowledge and data. We show first that the BN structure elicited from clinicians achieves a disappointing area under the ROC curve of 0.75 (± 0.03), whereas a structure learned by the CAMML hybrid causal discovery algorithm, which adheres with the temporal restrictions, achieves 0.81 (± 0.03). Second, our causal intervention results reveal that BN treatment recommendations, based on prescribing the treatment plan that maximises survival, can only predict the recorded treatment plan 29% of the time. However, this percentage rises to 76% when partial matches are included.

Original publication

DOI

10.1371/journal.pone.0082349

Type

Journal article

Journal

PLoS One

Publication Date

2013

Volume

8

Keywords

Algorithms, Area Under Curve, Artificial Intelligence, Bayes Theorem, Cluster Analysis, Databases, Factual, Decision Support Techniques, Delivery of Health Care, Humans, Lung Neoplasms, Neoplasm Staging, Prognosis, Reproducibility of Results